tbuckley/cabot-search huggingface dataset:
Abstract 5313: The expression of phospho-Rb S249, N-cadherin, E-cadherin, and B-catenin as a prostate proteomic score for the risk classification of prostate cancer tumors
Authors: Sheila M. Valle Cortes, Pedro Santiago Cardona, Carlos Diaz Osterman, Jaileene Perez Morales, Darielys Maldonado Maldonado, Gilberto Ruiz Deya
Journal: Cancer Research (2023)

Abstract Prostate cancer (PCa) ranks as the second leading cause of cancer-related deaths in American men. Most PCa deaths occur due to aggressive, fast-growing tumors that develop into metastasis when cancer cells spread to other body parts, including the lymph nodes, bones, etc. However, men could also be affected by indolent, slow-growing tumors, which may exist for an extended period without causing any symptoms or death. Current prognostic methods, such as Prolaris, or Oncotype, do not precisely specify how aggressive cancer might be, resulting in overtreating patients with indolent PCa. Therefore, improved ways to differentiate indolent from aggressive tumors are needed. In the presented study, we have validated the clinical potential of phospho-Rb S249, N-cadherin, B-catenin, and E-cadherin to discriminate between indolent and aggressive PCa tumors. We hypothesize that the biomarkers phospho-Rb S249, N-cadherin, E-cadherin, and B-catenin expression in PCa will be a clinical tool for the risk classification of PCa tumors when used as part of a prostate proteomic (PPS) that can predict the risk of the tumor of evolving into an aggressive phenotype. We conducted immunohistochemistry staining of these biomarkers on PCa tumor microarrays and correlated it with the patient’s clinical parameters, such as tumor size, lymph node invasion, metastasis, etc. Also, we correlated it with the PSA levels and created a PPS. The combination of our proteomic biomarkers predicts patient staging by 53.83%. Adding clinical data such as the tumor size, lymph node invasion, and metastasis (TNM) increases the precision of the prediction to 83.21%. In addition, the PSA levels increased as the phospho-Rb S249 expression and N-cadherin rise, accompanied by up-regulation of E-cadherin. Also, the PPS showed to distinguish between normal prostate tissue, hyperplasia, and PCa. Results suggest that these biomarkers could serve as a tool for the risk classification of indolent and aggressive PCa tumors. Citation Format: Sheila M. Valle Cortes, Pedro Santiago Cardona, Carlos Diaz Osterman, Jaileene Perez Morales, Darielys Maldonado Maldonado, Gilberto Ruiz Deya. The expression of phospho-Rb S249, N-cadherin, E-cadherin, and B-catenin as a prostate proteomic score for the risk classification of prostate cancer tumors. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5313.
Abstract 720: Implementation and evaluation of a mental health app with comedy content for women living with advanced cancer
Authors: Saranne Rothberg, Katherine B. Grill, Claire Wheeler
Journal: Cancer Research (2023)

Abstract Introduction: Metastatic cancer patients report elevated stress levels and unique stressors compared to other adults. This study tested the feasibility and efficacy of a mental health app with comedy content delivered to women living with advanced cancer (WLAC). Methods: The mental health platform Neolth was used as a self-guided intervention for WLAC, ages 18+ years. WLAC were recruited by The Comedy Cures Foundation through a press release, social media posts, an email newsletter, and a video call for participants on the foundation’s website. No compensation was provided to participants. After using the Neolth platform, which included watching comedy content for eight weeks, researchers evaluated engagement, and stress levels using The Perceived Stress Scale -10 (PSS-10), self-efficacy using The New General Self-Efficacy Scale (GSES), and anxiety and depression using The Hospital Anxiety and Depression Scale (HADS-A and HADS-D). Results: 54 WLAC (M = 52.2 years, R = 31-74) from 10 countries and 24 states participated by completing the baseline questionnaire. 20.5% identified as BIPOC and 33.0% had previous experience with a mental health app. 57.4% (N=31) completed the study surveys at Week 4 and Week 8. At Week 8, HADS-D scores decreased 18.1%, PSS-10 scores decreased 15.1%, HADS-A scores decreased by 9.7%, and GSES scores increased 7.6%. Participants spent an average of 116 minutes per week on Neolth with an average of 3.15 logins per week. 43.6% of time spent was completing self-guided relaxation practices and 17.2% watching Comedy Cures Foundation videos from cancer survivor Saranne Rothberg. The 30-day retention on Neolth was 100%, with 60-day retention 83%. Conclusion: This study demonstrated the ability of a mental health app with comedy content to engage and support the mental health of WLAC, presenting a cost-effective intervention. Further examination with a larger sample size is needed to conduct multivariate analyses for enhanced understanding of the role of mobile apps and comedy content in reducing stress, anxiety, and depression for WLAC. Citation Format: Saranne Rothberg, Katherine B. Grill, Claire Wheeler. Implementation and evaluation of a mental health app with comedy content for women living with advanced cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 720.
Abstract 4471: Reprogramming “cold” NF1 malignancies into “hot” tumors for immunotherapy
Authors: Laasya Madana, Lu Q. Le, Nipunika Somatilaka, Renee McKay
Journal: Cancer Research (2023)

Abstract Introduction: Neurofibromatosis Type 1 (NF1) results from mutations in the NF1 gene that inactivate the tumor suppressor, neurofibromin. This leads to hyperactive RAS signaling that predisposes NF1 patients to tumor development. Over time, some of these tumors can develop into malignant peripheral nerve sheath tumors (MPNST) which are currently incurable.Immune checkpoint blocking (ICB) programs a patient’s immune system to enhance tumor destruction. However, MPNSTs are cold tumors characterized by low immune cell presence in the tumor microenvironment. Interestingly, MPNSTs show checkpoint protein PD-L1 expression suggesting ICB as potential therapy for MPNST, given the ability to increase immune cell density in the tumor microenvironment. Activation of the intracellular receptor stimulator of interferon genes (STING) enhances antitumor immunity through the induction of pro-inflammatory cytokines and chemokines, including type I interferons. Cyclic GMP-AMP synthase (cGAS) is an enzyme that once bound to cytosolic DNA, synthesizes cyclic GMP-AMP (cGAMP), which activates STING to induce inflammatory cytokines and other immune mediators. Preclinical studies using mouse tumor models have assessed the efficacy of STING agonists that trigger the cGAS-STING-IFN axis, ultimately leading to augmented innate immunity and a T cell-rich tumor environment. Additionally, combining STING agonists with ICB has a synergistic effect in treating tumors. Therefore, we hypothesize that treatment with STING agonists would turn “cold” MPNSTs into “hot” tumors making them susceptible to targeting with ICB. Methods: We treated MPNST cell lines with synthetic STING agonist ADU-S100 for varying durations of 8, 18, 24, and 48 hours. At the end of the treatment, cells were harvested for qRT-PCR and immunoblot analysis. Results: We measured STING-IFN pathway activation through immunoblotting and observed that phosphorylation of STING effector proteins - a readout of STING activation – was increased followingby 8 hours after treatment. qRT-PCR analysis showed that STING target cytokine/chemokine gene expression was also upregulated by 8 hours after ADU-S100 treatment. Conclusions: Theise data demonstrates that ADU-S100 was able to activate the STING pathway in MPNST cell lines leading to proinflammatory cytokine/chemokine production. We will next test STING agonist in vivo use to determine whether ADU-S100 treatment inin mouse MPNST modelss of MPNST to is able to reprogram the tumor microenvironment into an immune inflamed one amenable to immunotherapy. Citation Format: Laasya Madana, Lu Q. Le, Nipunika Somatilaka, Renee McKay. Reprogramming “cold” NF1 malignancies into “hot” tumors for immunotherapy. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4471.
Abstract 6563: Glioma stem cell index recapitulates grade, IDH mutation status and correlates with survival of glioma patients
Authors: Renan L. Simões, Maycon Marção, Tathiane M. Malta
Journal: Cancer Research (2023)

Abstract Adult diffuse gliomas are heterogeneous and the most common primary brain tumors. Gliomas have high tissue invasion, proliferation and therapeutic resistance potential. Although much knowledge has been recently gained regarding glioma biology and evolution, many questions are still open, such as which is the cell of origin, what are its characteristics and how the tumor propagation occurs. The cancer stem cell (CSC) model proposes a population of cells with high self-renew capacity and capable of propagating the tumor with more differentiated cells, generating intratumoral heterogeneity. Based on the CSC model, our work aims to integrate the most advanced techniques available such as scRNAseq and machine learning models to estimate the enrichment of glioma stem cells (GSCs) in tumors, by defining a GSC-Stemness Index (GSCsi). To build the prediction model, we used public scRNA-seq data from glioblastoma (GBM)-enriched GSCs. We used the standard Seurat pipeline for quality control, normalization and downstream analysis. The GSC single cell data was used to train a prediction model using the One Class Logistic Regression (OCLR) algorithm. Several models were tested, including overdispersed genes, differentially expressed genes between GSCs and the whole tumor, GSCs from each patient individually and a model with all GSCs. The prediction models were applied to gene expression data from TCGA, GLASS, and publicly available scRNA-seq data from different glioma subtypes. The model built with all the GSCs and all genes showed the best performance, being able to identify with higher indices grade 4 gliomas and IDHwt in the TCGA and GLASS data. Survival analysis resulted in a hazard ratio greater than 20, indicating a high correlation between increased GSCsi and poor prognosis. By applying the model to scRNA-seq data from gliomas, clusters of high GSCsi were identified. We can partially conclude that the GSCsi obtained with the model is capable of identifying grade 4 gliomas and IDHwt and we are performing analyzes with the genes with the highest correlation (positive and negative) with the GSCsi in the TCGA and GLASS data. The analysis of these genes together with genes differentially expressed in the high GSCsi clusters in scRNA-seq data can elucidate pathways responsible for the therapeutic resistance and propagation of gliomas, in addition to proposing theories about the cell of origin and potential therapeutic targets to improve the diagnosis and treatment of these patients. Citation Format: Renan L. Simões, Maycon Marção, Tathiane M. Malta. Glioma stem cell index recapitulates grade, IDH mutation status and correlates with survival of glioma patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6563.
Abstract 6592: An AI platform for vascular and fibrosis analysis of pathology images
Authors: Yawen Zheng, Likun Zhang, Dawei Wang, Sheng Gao
Journal: Cancer Research (2023)

Abstract Introduction: Changes in the number of blood vessels and the proportion of fibrosis regions in xenograft tumors are key indicators for efficacy of oncology drugs, which currently are assessed mainly by human visual examination that is time-consuming, inaccurate and biased because only part of a pathology image is checked. We developed an online AI platform that integrates machine learning algorithms in Computer Vision to quickly and automatically obtain accurate and comprehensive information on blood vessels and fibrosis regions from a whole pathology image. Methods: In a stained pathology image, blood vessels are stained with a different color than other components (e. g. connective tissues, tumor cells, background), so we can directly locate pixels representing them. They also have different topological structures, so we can outline and count the contours of vessels. To count the number of vessels more precisely and reduce the interference of noise, we keep a contour only if it intersects with some located pixels and the number of pixels in the intersection exceeds an adjustable threshold. The fibrosis regions are identified by color contrast since they are stained with a distinct and easily recognizable color. The algorithms were implemented using the OpenCV package. Results: Our AI platform automatically annotates blood vessels and fibrosis regions in pathology images, and generates reports in excel format to report the number and total area of blood vessels, the area and ratio of fibrosis regions, and execution time. It demonstrates superior performance to standard pathology software. Citation Format: Yawen Zheng, Likun Zhang, Dawei Wang, Sheng Gao. An AI platform for vascular and fibrosis analysis of pathology images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6592.
Abstract 786: Single molecule methylation sequencing of circulating DNA identifies individuals with breast cancer
Authors: Billy T. Lau, Xiangqi Bai, Alison Notestine, Brett Parkinson, Hanlee P. Ji, Lincoln Nadauld
Journal: Cancer Research (2023)

Abstract Liquid biopsies are being investigated for the detection of cancer from blood. Epigenetic characterization of cell free DNA (cfDNA), specifically DNA methylation, is an emerging approach for sensitive detection and quantification of tumor burden. For breast cancer detection from blood, existing approaches for testing cfDNA have demonstrated a low performance in identifying affected patients. For this study, we developed a new approach that overcame this issue. Addressing the challenge of detecting breast cancer from cfDNA, this new approach relied on single-molecule methylation profiling of cfDNA. We used nanopore single molecule sequencing to provide quantitative characterization of cfDNA methylomes. This study determined if this approach provided the methylation features required for detecting the presence of breast cancer. The study population consisted of an extended cohort (~200 with untreated breast cancer and ~400 healthy controls). We identified 5mC-specific profiles from the nanopore sequencing signals of cfDNA. Importantly, this approach did not require any biosulfite or biochemical processing steps for determining methylation. Thus, we determined methylation profiles that were directly extrapolated from native cfDNA at single molecule resolution. Our results from this study demonstrated higher sensitivity of detecting breast cancer compared to other cfDNA methylation tests. We developed a new analytical framework for characterizing the methylation profiles of individuals with breast cancer compared to healthy individuals. Using a supervised bootstrapped algorithm, we identified CpG sites that were differentially methylated between the case and control groups. Afterwards, we calculated the similarity of any sample’s methylation profile to a training data. Our method also accommodates sparse and missing data. Based on separate validation datasets, we accurately identified those individuals with breast cancer. We validated our study with a different, independent cohort of healthy controls and individuals with breast cancer. Overall, this study demonstrates that a nanopore-based cfDNA sequencing and methylation profiling detects breast cancer from blood with higher performance then has been previously reported. Citation Format: Billy T. Lau, Xiangqi Bai, Alison Notestine, Brett Parkinson, Hanlee P. Ji, Lincoln Nadauld. Single molecule methylation sequencing of circulating DNA identifies individuals with breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 786.
Abstract 4320: A novel digitally directed tissue microdissection platform integrates digital pathology with molecular analysis: Case study in metastatic colorectal cancer
Authors: John H. Butler, Bidhan Chaudhuri, Katie Konigsfeld, Rish Pai
Journal: Cancer Research (2023)

Abstract Artificial intelligence (AI) based digital pathology has been getting rapid adoption over the last 2 to 3 years These AI based digital pathology models can analyze samples at high resolution and scale Retaining that resolution at scale through molecular analysis is key to the discovery of novel biomarkers We have developed a technology that can integrate AI-supported digital pathology (DP) algorithms to identify informative tissue regions for selective microdissection to improve molecular analysis of FFPE material The system is capable of extracting DNA, RNA or proteins with cellular (10 micron) resolution from FFPE tissue sections. The samples are ready for use in off-the-shelf molecular analysis workflows (qPCR, Next Gen Sequencing, etc) The system decreases the tumor cellularity requirements for clinical analysis by extracting only the area enriched for informative biomarkers To demonstrate the capability of the system we used an AI based digital pathology model that was developed to identify multiple cellular phenotypes from a H&amp;E stained colorectal cancer FFPE tissue sections We used the digital pathology output to guide the microdissection of RNA from tumor buds, carcinoma, immature stroma and inflammatory stroma in a paired designed RNA seq experiment The data showed that the system is capable of enriching the tumor-bud tissue content from&amp;lt;1% in the tissue to ~50% purity in the crude lysate Bioinformatic analysis of the RNAseq signatures showed that the tumor buds had a distinct RNAseq signature relative to the other three cell types and that the gene expression pattern was consistent with EMT pathways. The methodology is scalable, robust, and simpler than current microdissection techniques. This technology can be used to develop novel tests by integrating with AI algorithms, targeting tissue enriched for predictive biomarkers, and enabling low tumor cellularity samples to be analyzed with high precision. Citation Format: John H. Butler, Bidhan Chaudhuri, Katie Konigsfeld, Rish Pai. A novel digitally directed tissue microdissection platform integrates digital pathology with molecular analysis: Case study in metastatic colorectal cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4320.
Abstract 991: Leveraging sequences missing from the human genome to detect cancer
Authors: Ofer Yizhar Barnea, Ilias Georgakopoulos-Soares, Nadav Ahituv, Jocelyn Chapman, Martin Hemberg, Ioannis Mouratidis, Konstantinos Syrigos, Nikolaos Syrigos, Ioannis Vathiotis, Mayank Mahajan, Emmanouil Panagiotou, Andriani Charpidou, Mark Kvale, Candace S. Chan, Ryder Easterlin
Journal: Cancer Research (2023)

Abstract Introduction: Cancer diagnosis using cell-free DNA (cfDNA) has the potential to improve treatment and survival but has several technical limitations. Methods: Here, we used neomers, short DNA sequences (13-17bp in length) that are largely absent from the healthy human genome, but appear in the tumor genome due to somatic mutations, to detect cancer at early stages from cfDNA. cfDNA was extracted from 1mL of plasma, and utilized from Whole Genome Sequencing at 5X coverage. Results: First, we analyzed over 2,500 cancer whole-genome sequences (WGS), to show that a neomer-based classifier can distinguish twenty-one different tumor types with higher accuracy than state-of-the-art methods. Refinement of this classifier via manually selected neomer sets, identified additional cancer features with greater precision. We next generated cfDNA WGS data from &amp;gt;600 patients and non-cancerous controls across four different types of cancers (prostate, lung, ovarian, colorectal), and demonstrated that neomers can precisely identify cancer from a limited amount of plasma for all four cancer types, with exampled sensitivities of 93.73% and 73.47% for lung and ovarian cancers, respectively. Additionally, using both luciferase and massively parallel reporter assays (MPRAs), we show that our neomer-driven approach can detect cancer-associated non-coding mutations that affect gene regulatory activity. Conclusions: Our results show that neomers provide a novel, sensitive, specific and potentially broadly applicable cancer detection tool. This approach has several advantages over existing strategies for liquid biopsies: 1) it is based on standard next-generation sequencing which is already widely available in the clinic; 2) it requires low amounts of starting material and is completely tumor agnostic; 3) it searches the entire genome and is not restricted to known driver mutations. 4) it allows the detection of early-stage disease. Citation Format: Ofer Yizhar Barnea, Ilias Georgakopoulos-Soares, Nadav Ahituv, Jocelyn Chapman, Martin Hemberg, Ioannis Mouratidis, Konstantinos Syrigos, Nikolaos Syrigos, Ioannis Vathiotis, Mayank Mahajan, Emmanouil Panagiotou, Andriani Charpidou, Mark Kvale, Candace S. Chan, Ryder Easterlin. Leveraging sequences missing from the human genome to detect cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 991.